Overview

Dataset statistics

Number of variables16
Number of observations891
Missing cells177
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory150.6 KiB
Average record size in memory173.1 B

Variable types

Numeric8
Boolean1
Categorical3
Text3
DateTime1

Alerts

Birthday has 177 (19.9%) missing valuesMissing
Novos_IDs is uniformly distributedUniform
PassengerId is uniformly distributedUniform
Novos_IDs has unique valuesUnique
PassengerId has unique valuesUnique
Name has unique valuesUnique
Distribuicao has unique valuesUnique
SibSp has 608 (68.2%) zerosZeros
Parch has 678 (76.1%) zerosZeros
Fare has 15 (1.7%) zerosZeros
%_Fare has 15 (1.7%) zerosZeros

Reproduction

Analysis started2024-06-04 21:44:57.449359
Analysis finished2024-06-04 21:47:24.361679
Duration2 minutes and 26.91 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Novos_IDs
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct891
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1336
Minimum891
Maximum1781
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.2 KiB
2024-06-04T18:47:24.580376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum891
5-th percentile935.5
Q11113.5
median1336
Q31558.5
95-th percentile1736.5
Maximum1781
Range890
Interquartile range (IQR)445

Descriptive statistics

Standard deviation257.35384
Coefficient of variation (CV)0.19263012
Kurtosis-1.2
Mean1336
Median Absolute Deviation (MAD)223
Skewness0
Sum1190376
Variance66231
MonotonicityStrictly increasing
2024-06-04T18:47:24.862331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
891 1
 
0.1%
1489 1
 
0.1%
1478 1
 
0.1%
1479 1
 
0.1%
1480 1
 
0.1%
1481 1
 
0.1%
1482 1
 
0.1%
1483 1
 
0.1%
1484 1
 
0.1%
1485 1
 
0.1%
Other values (881) 881
98.9%
ValueCountFrequency (%)
891 1
0.1%
892 1
0.1%
893 1
0.1%
894 1
0.1%
895 1
0.1%
896 1
0.1%
897 1
0.1%
898 1
0.1%
899 1
0.1%
900 1
0.1%
ValueCountFrequency (%)
1781 1
0.1%
1780 1
0.1%
1779 1
0.1%
1778 1
0.1%
1777 1
0.1%
1776 1
0.1%
1775 1
0.1%
1774 1
0.1%
1773 1
0.1%
1772 1
0.1%

PassengerId
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct891
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean446
Minimum1
Maximum891
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.2 KiB
2024-06-04T18:47:25.097208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile45.5
Q1223.5
median446
Q3668.5
95-th percentile846.5
Maximum891
Range890
Interquartile range (IQR)445

Descriptive statistics

Standard deviation257.35384
Coefficient of variation (CV)0.57702655
Kurtosis-1.2
Mean446
Median Absolute Deviation (MAD)223
Skewness0
Sum397386
Variance66231
MonotonicityStrictly increasing
2024-06-04T18:47:25.352221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
599 1
 
0.1%
588 1
 
0.1%
589 1
 
0.1%
590 1
 
0.1%
591 1
 
0.1%
592 1
 
0.1%
593 1
 
0.1%
594 1
 
0.1%
595 1
 
0.1%
Other values (881) 881
98.9%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
891 1
0.1%
890 1
0.1%
889 1
0.1%
888 1
0.1%
887 1
0.1%
886 1
0.1%
885 1
0.1%
884 1
0.1%
883 1
0.1%
882 1
0.1%

Survived
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size40.1 KiB
False
549 
True
342 
ValueCountFrequency (%)
False 549
61.6%
True 342
38.4%
2024-06-04T18:47:25.621376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Pclass
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size46.2 KiB
3
491 
1
216 
2
184 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row3
4th row1
5th row3

Common Values

ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Length

2024-06-04T18:47:25.819665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T18:47:26.061969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring characters

ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Name
Text

UNIQUE 

Distinct891
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size46.2 KiB
2024-06-04T18:47:26.552707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length82
Median length52
Mean length26.965208
Min length12

Characters and Unicode

Total characters24026
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique891 ?
Unique (%)100.0%

Sample

1st rowBraund, Mr. Owen Harris
2nd rowCumings, Mrs. John Bradley (Florence Briggs Thayer)
3rd rowHeikkinen, Miss. Laina
4th rowFutrelle, Mrs. Jacques Heath (Lily May Peel)
5th rowAllen, Mr. William Henry
ValueCountFrequency (%)
mr 521
 
14.4%
miss 182
 
5.0%
mrs 129
 
3.6%
william 64
 
1.8%
john 44
 
1.2%
master 40
 
1.1%
henry 35
 
1.0%
george 24
 
0.7%
james 24
 
0.7%
charles 23
 
0.6%
Other values (1515) 2538
70.0%
2024-06-04T18:47:27.450459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2735
 
11.4%
r 1958
 
8.1%
e 1703
 
7.1%
a 1657
 
6.9%
i 1325
 
5.5%
n 1304
 
5.4%
s 1297
 
5.4%
M 1128
 
4.7%
l 1067
 
4.4%
o 1008
 
4.2%
Other values (50) 8844
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24026
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2735
 
11.4%
r 1958
 
8.1%
e 1703
 
7.1%
a 1657
 
6.9%
i 1325
 
5.5%
n 1304
 
5.4%
s 1297
 
5.4%
M 1128
 
4.7%
l 1067
 
4.4%
o 1008
 
4.2%
Other values (50) 8844
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24026
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2735
 
11.4%
r 1958
 
8.1%
e 1703
 
7.1%
a 1657
 
6.9%
i 1325
 
5.5%
n 1304
 
5.4%
s 1297
 
5.4%
M 1128
 
4.7%
l 1067
 
4.4%
o 1008
 
4.2%
Other values (50) 8844
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24026
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2735
 
11.4%
r 1958
 
8.1%
e 1703
 
7.1%
a 1657
 
6.9%
i 1325
 
5.5%
n 1304
 
5.4%
s 1297
 
5.4%
M 1128
 
4.7%
l 1067
 
4.4%
o 1008
 
4.2%
Other values (50) 8844
36.8%

Sex
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size46.2 KiB
male
577 
female
314 

Length

Max length6
Median length4
Mean length4.704826
Min length4

Characters and Unicode

Total characters4192
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowfemale
4th rowfemale
5th rowmale

Common Values

ValueCountFrequency (%)
male 577
64.8%
female 314
35.2%

Length

2024-06-04T18:47:27.697715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T18:47:27.884928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
male 577
64.8%
female 314
35.2%

Most occurring characters

ValueCountFrequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4192
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4192
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4192
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Birthday
Date

MISSING 

Distinct71
Distinct (%)9.9%
Missing177
Missing (%)19.9%
Memory size46.2 KiB
Minimum1832-04-15 00:00:00
Maximum1912-04-15 00:00:00
2024-06-04T18:47:28.061129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:47:28.307000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

SibSp
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52300786
Minimum0
Maximum8
Zeros608
Zeros (%)68.2%
Negative0
Negative (%)0.0%
Memory size46.2 KiB
2024-06-04T18:47:28.628365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1027434
Coefficient of variation (CV)2.1084644
Kurtosis17.88042
Mean0.52300786
Median Absolute Deviation (MAD)0
Skewness3.6953517
Sum466
Variance1.2160431
MonotonicityNot monotonic
2024-06-04T18:47:28.882103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 608
68.2%
1 209
 
23.5%
2 28
 
3.1%
4 18
 
2.0%
3 16
 
1.8%
8 7
 
0.8%
5 5
 
0.6%
ValueCountFrequency (%)
0 608
68.2%
1 209
 
23.5%
2 28
 
3.1%
3 16
 
1.8%
4 18
 
2.0%
5 5
 
0.6%
8 7
 
0.8%
ValueCountFrequency (%)
8 7
 
0.8%
5 5
 
0.6%
4 18
 
2.0%
3 16
 
1.8%
2 28
 
3.1%
1 209
 
23.5%
0 608
68.2%

Parch
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.38159371
Minimum0
Maximum6
Zeros678
Zeros (%)76.1%
Negative0
Negative (%)0.0%
Memory size46.2 KiB
2024-06-04T18:47:29.119901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.80605722
Coefficient of variation (CV)2.1123441
Kurtosis9.7781252
Mean0.38159371
Median Absolute Deviation (MAD)0
Skewness2.749117
Sum340
Variance0.64972824
MonotonicityNot monotonic
2024-06-04T18:47:29.285293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 678
76.1%
1 118
 
13.2%
2 80
 
9.0%
5 5
 
0.6%
3 5
 
0.6%
4 4
 
0.4%
6 1
 
0.1%
ValueCountFrequency (%)
0 678
76.1%
1 118
 
13.2%
2 80
 
9.0%
3 5
 
0.6%
4 4
 
0.4%
5 5
 
0.6%
6 1
 
0.1%
ValueCountFrequency (%)
6 1
 
0.1%
5 5
 
0.6%
4 4
 
0.4%
3 5
 
0.6%
2 80
 
9.0%
1 118
 
13.2%
0 678
76.1%

Ticket
Text

Distinct681
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Memory size46.2 KiB
2024-06-04T18:47:29.699733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length18
Median length17
Mean length6.7508418
Min length3

Characters and Unicode

Total characters6015
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique547 ?
Unique (%)61.4%

Sample

1st rowA/5 21171
2nd rowPC 17599
3rd rowSTON/O2. 3101282
4th row113803
5th row373450
ValueCountFrequency (%)
pc 60
 
5.3%
c.a 27
 
2.4%
a/5 17
 
1.5%
ca 14
 
1.2%
ston/o 12
 
1.1%
2 12
 
1.1%
sc/paris 9
 
0.8%
w./c 9
 
0.8%
soton/o.q 8
 
0.7%
347082 7
 
0.6%
Other values (709) 955
84.5%
2024-06-04T18:47:30.375978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 746
12.4%
1 689
11.5%
2 594
9.9%
7 490
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.7%
5 387
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6015
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 746
12.4%
1 689
11.5%
2 594
9.9%
7 490
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.7%
5 387
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6015
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 746
12.4%
1 689
11.5%
2 594
9.9%
7 490
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.7%
5 387
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6015
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 746
12.4%
1 689
11.5%
2 594
9.9%
7 490
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.7%
5 387
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Fare
Real number (ℝ)

ZEROS 

Distinct248
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.204208
Minimum0
Maximum512.3292
Zeros15
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size46.2 KiB
2024-06-04T18:47:30.635573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.225
Q17.9104
median14.4542
Q331
95-th percentile112.07915
Maximum512.3292
Range512.3292
Interquartile range (IQR)23.0896

Descriptive statistics

Standard deviation49.693429
Coefficient of variation (CV)1.5430725
Kurtosis33.398141
Mean32.204208
Median Absolute Deviation (MAD)6.9042
Skewness4.7873165
Sum28693.949
Variance2469.4368
MonotonicityNot monotonic
2024-06-04T18:47:30.874833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.05 43
 
4.8%
13 42
 
4.7%
7.8958 38
 
4.3%
7.75 34
 
3.8%
26 31
 
3.5%
10.5 24
 
2.7%
7.925 18
 
2.0%
7.775 16
 
1.8%
7.2292 15
 
1.7%
0 15
 
1.7%
Other values (238) 615
69.0%
ValueCountFrequency (%)
0 15
1.7%
4.0125 1
 
0.1%
5 1
 
0.1%
6.2375 1
 
0.1%
6.4375 1
 
0.1%
6.45 1
 
0.1%
6.4958 2
 
0.2%
6.75 2
 
0.2%
6.8583 1
 
0.1%
6.95 1
 
0.1%
ValueCountFrequency (%)
512.3292 3
0.3%
263 4
0.4%
262.375 2
0.2%
247.5208 2
0.2%
227.525 4
0.4%
221.7792 1
 
0.1%
211.5 1
 
0.1%
211.3375 3
0.3%
164.8667 2
0.2%
153.4625 3
0.3%

Cabin
Text

Distinct148
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Memory size46.2 KiB
2024-06-04T18:47:31.284979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length15
Median length12
Mean length10.074074
Min length1

Characters and Unicode

Total characters8976
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique101 ?
Unique (%)11.3%

Sample

1st rowDesconhecido
2nd rowC85
3rd rowDesconhecido
4th rowC123
5th rowDesconhecido
ValueCountFrequency (%)
desconhecido 687
74.3%
c25 4
 
0.4%
c27 4
 
0.4%
g6 4
 
0.4%
b96 4
 
0.4%
b98 4
 
0.4%
f 4
 
0.4%
c23 4
 
0.4%
f33 3
 
0.3%
e101 3
 
0.3%
Other values (152) 204
 
22.1%
2024-06-04T18:47:31.949630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 1374
15.3%
o 1374
15.3%
e 1374
15.3%
D 721
8.0%
s 687
7.7%
n 687
7.7%
h 687
7.7%
i 687
7.7%
d 687
7.7%
2 72
 
0.8%
Other values (17) 626
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8976
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 1374
15.3%
o 1374
15.3%
e 1374
15.3%
D 721
8.0%
s 687
7.7%
n 687
7.7%
h 687
7.7%
i 687
7.7%
d 687
7.7%
2 72
 
0.8%
Other values (17) 626
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8976
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 1374
15.3%
o 1374
15.3%
e 1374
15.3%
D 721
8.0%
s 687
7.7%
n 687
7.7%
h 687
7.7%
i 687
7.7%
d 687
7.7%
2 72
 
0.8%
Other values (17) 626
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8976
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 1374
15.3%
o 1374
15.3%
e 1374
15.3%
D 721
8.0%
s 687
7.7%
n 687
7.7%
h 687
7.7%
i 687
7.7%
d 687
7.7%
2 72
 
0.8%
Other values (17) 626
7.0%

Embarked
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size46.2 KiB
S
644 
C
168 
Q
77 
D
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowC
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S 644
72.3%
C 168
 
18.9%
Q 77
 
8.6%
D 2
 
0.2%

Length

2024-06-04T18:47:32.107917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T18:47:32.298929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
s 644
72.3%
c 168
 
18.9%
q 77
 
8.6%
d 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
S 644
72.3%
C 168
 
18.9%
Q 77
 
8.6%
D 2
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 644
72.3%
C 168
 
18.9%
Q 77
 
8.6%
D 2
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 644
72.3%
C 168
 
18.9%
Q 77
 
8.6%
D 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 644
72.3%
C 168
 
18.9%
Q 77
 
8.6%
D 2
 
0.2%

Age
Real number (ℝ)

Distinct72
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.698101
Minimum0
Maximum80.052055
Zeros7
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size46.2 KiB
2024-06-04T18:47:32.477714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.0054795
Q122.013699
median29.698101
Q335.021918
95-th percentile54.035616
Maximum80.052055
Range80.052055
Interquartile range (IQR)13.008219

Descriptive statistics

Standard deviation13.019444
Coefficient of variation (CV)0.43839315
Kurtosis0.97154209
Mean29.698101
Median Absolute Deviation (MAD)6.3238172
Skewness0.42886103
Sum26461.008
Variance169.50592
MonotonicityNot monotonic
2024-06-04T18:47:32.691863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.69810061 177
 
19.9%
24.01369863 31
 
3.5%
22.01369863 27
 
3.0%
28.01643836 27
 
3.0%
30.01917808 27
 
3.0%
18.0109589 26
 
2.9%
19.0109589 25
 
2.8%
21.01369863 24
 
2.7%
36.02191781 23
 
2.6%
25.01643836 23
 
2.6%
Other values (62) 481
54.0%
ValueCountFrequency (%)
0 7
0.8%
1.002739726 7
0.8%
2.002739726 10
1.1%
3.002739726 6
0.7%
4.002739726 10
1.1%
5.005479452 4
 
0.4%
6.005479452 3
 
0.3%
7.005479452 3
 
0.3%
8.005479452 4
 
0.4%
9.008219178 8
0.9%
ValueCountFrequency (%)
80.05205479 1
 
0.1%
74.04931507 1
 
0.1%
71.04657534 2
0.2%
70.04657534 3
0.3%
66.04383562 1
 
0.1%
65.04383562 3
0.3%
64.04109589 2
0.2%
63.04109589 2
0.2%
62.04109589 4
0.4%
61.04109589 3
0.3%

Distribuicao
Real number (ℝ)

UNIQUE 

Distinct891
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.026916963
Minimum-3.2104308
Maximum3.4011057
Zeros0
Zeros (%)0.0%
Negative455
Negative (%)51.1%
Memory size46.2 KiB
2024-06-04T18:47:32.954203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-3.2104308
5-th percentile-1.6467905
Q1-0.68669182
median-0.030667987
Q30.66276317
95-th percentile1.545004
Maximum3.4011057
Range6.6115365
Interquartile range (IQR)1.349455

Descriptive statistics

Standard deviation0.98739742
Coefficient of variation (CV)-36.683092
Kurtosis0.055780862
Mean-0.026916963
Median Absolute Deviation (MAD)0.67393677
Skewness0.09236407
Sum-23.983014
Variance0.97495367
MonotonicityNot monotonic
2024-06-04T18:47:33.170047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.232425872 1
 
0.1%
1.213236433 1
 
0.1%
0.311882012 1
 
0.1%
-0.0319922578 1
 
0.1%
1.052720735 1
 
0.1%
-1.363281409 1
 
0.1%
-0.3217057353 1
 
0.1%
-0.009375088178 1
 
0.1%
-1.833945996 1
 
0.1%
-0.6850577619 1
 
0.1%
Other values (881) 881
98.9%
ValueCountFrequency (%)
-3.210430797 1
0.1%
-2.693956317 1
0.1%
-2.495752882 1
0.1%
-2.364696081 1
0.1%
-2.326926212 1
0.1%
-2.319847554 1
0.1%
-2.280459826 1
0.1%
-2.271028443 1
0.1%
-2.236053777 1
0.1%
-2.233052848 1
0.1%
ValueCountFrequency (%)
3.401105719 1
0.1%
3.101863538 1
0.1%
2.966250097 1
0.1%
2.801153155 1
0.1%
2.611365467 1
0.1%
2.592084067 1
0.1%
2.533515016 1
0.1%
2.503354047 1
0.1%
2.489707532 1
0.1%
2.438510067 1
0.1%

%_Fare
Real number (ℝ)

ZEROS 

Distinct248
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11223345
Minimum0
Maximum1.7854956
Zeros15
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size46.2 KiB
2024-06-04T18:47:33.401644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.025179525
Q10.027568181
median0.050373686
Q30.10803671
95-th percentile0.39060204
Maximum1.7854956
Range1.7854956
Interquartile range (IQR)0.080468533

Descriptive statistics

Standard deviation0.17318435
Coefficient of variation (CV)1.5430725
Kurtosis33.398141
Mean0.11223345
Median Absolute Deviation (MAD)0.024061519
Skewness4.7873165
Sum100
Variance0.029992818
MonotonicityNot monotonic
2024-06-04T18:47:33.607246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02805469514 43
 
4.8%
0.04530571886 42
 
4.7%
0.02751729961 38
 
4.3%
0.02700917855 34
 
3.8%
0.09061143772 31
 
3.5%
0.03659308062 24
 
2.7%
0.02761906323 18
 
2.0%
0.02709630493 16
 
1.8%
0.02519416175 15
 
1.7%
0 15
 
1.7%
Other values (238) 615
69.0%
ValueCountFrequency (%)
0 15
1.7%
0.01398378438 1
 
0.1%
0.01742527649 1
 
0.1%
0.02173803242 1
 
0.1%
0.02243504347 1
 
0.1%
0.02247860667 1
 
0.1%
0.0226382222 2
 
0.2%
0.02352412325 2
 
0.2%
0.02390155474 1
 
0.1%
0.02422113431 1
 
0.1%
ValueCountFrequency (%)
1.785495592 3
0.3%
0.9165695431 4
0.4%
0.9143913836 2
0.2%
0.8626236752 2
0.2%
0.7929372065 4
0.4%
0.7729127757 1
 
0.1%
0.7370891953 1
 
0.1%
0.7365228738 3
0.3%
0.5745695661 2
0.2%
0.5348252985 3
0.3%

Interactions

2024-06-04T18:47:11.120033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:44:58.023868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:45:39.720323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:13.214416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:25.256590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:36.072194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:47.756487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:47:00.406145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:47:16.612754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:45:10.351205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:45:47.220821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:18.690360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:29.544012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:41.268551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:53.375004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:47:05.244656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:47:21.956731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:45:19.369125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:45:54.673830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:23.427859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:34.045076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:46.353493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:59.015434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:47:09.821370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:47:22.188273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:45:22.510930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:45:57.811772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:23.601376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:34.364695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:46.536257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:59.259003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:47:10.016251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:47:22.431389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:45:25.960915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:00.833207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:23.778487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:34.635590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:46.760119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:59.490469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:47:10.233480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:47:22.692807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:45:29.959833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:04.302565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:24.002657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:35.038349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:47.040885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:59.699542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:47:10.441963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:47:23.064984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:45:32.876122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:06.934319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:24.876478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:35.337768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:47.262025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:59.947244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:47:10.663246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:47:23.327154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:45:36.016865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:10.256844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:25.063411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:35.731280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:46:47.451243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:47:00.165571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-04T18:47:10.874144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Missing values

2024-06-04T18:47:23.724296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-04T18:47:24.209191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Novos_IDsPassengerIdSurvivedPclassNameSexBirthdaySibSpParchTicketFareCabinEmbarkedAgeDistribuicao%_Fare
0000008911No3Braund, Mr. Owen Harrismale1890-04-1510A/5 211717.2500DesconhecidoS22.01370-0.232430.02527
1000008922Yes1Cumings, Mrs. John Bradley (Florence Briggs Thayer)female1874-04-1510PC 1759971.2833C85C38.024660.710970.24843
2000008933Yes3Heikkinen, Miss. Lainafemale1886-04-1500STON/O2. 31012827.9250DesconhecidoS26.016440.453160.02762
3000008944Yes1Futrelle, Mrs. Jacques Heath (Lily May Peel)female1877-04-151011380353.1000C123S35.02192-0.332680.18506
4000008955No3Allen, Mr. William Henrymale1877-04-15003734508.0500DesconhecidoS35.02192-0.384460.02805
5000008966No3Moran, Mr. JamesmaleNaT003308778.4583DesconhecidoQ29.69810-0.530330.02948
6000008977No1McCarthy, Mr. Timothy Jmale1858-04-15001746351.8625E46S54.035621.350310.18074
7000008988No3Palsson, Master. Gosta Leonardmale1910-04-153134990921.0750DesconhecidoS2.002742.015220.07345
8000008999Yes3Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female1885-04-150234774211.1333DesconhecidoS27.01644-0.057530.03880
90000090010Yes2Nasser, Mrs. Nicholas (Adele Achem)female1898-04-151023773630.0708DesconhecidoC14.008220.564650.10480
Novos_IDsPassengerIdSurvivedPclassNameSexBirthdaySibSpParchTicketFareCabinEmbarkedAgeDistribuicao%_Fare
88100001772882No3Markun, Mr. Johannmale1879-04-15003492577.8958DesconhecidoS33.021920.981850.02752
88200001773883No3Dahlberg, Miss. Gerda Ulrikafemale1890-04-1500755210.5167DesconhecidoS22.013700.107290.03665
88300001774884No2Banfield, Mr. Frederick Jamesmale1884-04-1500C.A./SOTON 3406810.5000DesconhecidoS28.016440.434770.03659
88400001775885No3Sutehall, Mr. Henry Jrmale1887-04-1500SOTON/OQ 3920767.0500DesconhecidoS25.01644-1.361330.02457
88500001776886No3Rice, Mrs. William (Margaret Norton)female1873-04-150538265229.1250DesconhecidoQ39.02466-1.821480.10150
88600001777887No2Montvila, Rev. Juozasmale1885-04-150021153613.0000DesconhecidoS27.016441.100020.04531
88700001778888Yes1Graham, Miss. Margaret Edithfemale1893-04-150011205330.0000B42S19.010962.801150.10455
88800001779889No3Johnston, Miss. Catherine Helen "Carrie"femaleNaT12W./C. 660723.4500DesconhecidoS29.698101.052330.08172
88900001780890Yes1Behr, Mr. Karl Howellmale1886-04-150011136930.0000C148C26.01644-0.453060.10455
89000001781891No3Dooley, Mr. Patrickmale1880-04-15003703767.7500DesconhecidoQ32.019180.534650.02701